Jianzhen Liu

Jianzhen Liu is an Assistant Professor of Teaching at the Institute for Artificial Intelligence and Data Science within the School of Engineering and Applied Sciences at the University at Buffalo, where he focuses on foundational mathematical research supporting data science and artificial intelligence applications.

His educational qualifications include:

• MS in Analytics, Georgia Institute of Technology, 2024
• PhD in Mathematics, Auburn University, 2017
• MS in Mathematics, Beijing University of Technology, China, 2007
• BS in Mathematics, Anyang Normal University, China, 2004

Dr. Liu's research centers on linear algebra and matrix theory, with significant contributions to positive partial transpose matrices, block matrix structures, Toeplitz systems, and quaternion-based matrix equations. His work bridges pure mathematics and computational applications, particularly in quantum information theory and data analysis frameworks. The theoretical depth of his publications demonstrates consistent expertise in matrix decompositions and algebraic structures.

His publication history reveals a sustained focus on matrix theory since 2007, with recent work (2017-2020) emphasizing structural properties of matrices relevant to quantum computing and data science. This trajectory shows increasing alignment with his current role in an AI-focused institute.

Scientific recognition includes:

• Tin-Yau Tam Graduate Fellowship, Auburn University, 2016
• Ralph B. Bennett Graduate Fellowship, Auburn University, 2015
• Don and Sandy Logan Graduate Fellowship, Auburn University, 2014

No information is available regarding graduate student supervision or externally funded research grants. Similarly, details about laboratory facilities or research team leadership could not be determined from the provided text.